The wealth of information produced by omics experiments on cardiovascular disease remains largely underexplored. As high-throughput technologies expand, more and more research scientists are faced with large data sets that are difficult to manage on their own. Modelling is a systems medicine approach, which has become a tractable method to parse complex and large datasets. By performing a modelling approach (also called an in silico approach) a vast quantity of data can be represented in a comprehensible format, making it possible to interrogate pooled data sets and reveal connections that might otherwise be overlooked.
The sysVASC consortium is using cutting edge modelling and simulation methods to characterize the causal chain of events and molecular structures involved in cardiovascular disease. The project already has access to over 9.000 high-quality patient data sets. The project plans to build upon this information in a specialized cardiovascular disease database, which will additionally contain information from other available databases and systematic literature searches. Combined, this database will be an invaluable resource for cardiovascular research and for the identification of novel therapeutic targets.
Methods will be developed to automate the input of available large-scale datasets, bioinformatics services, tools for proteomic data annotation and curation (Swiss-Prot group), protease identification (Proteasix, TopFIND), and text-mining for linking proteases to pathways (Pubmed, Gene Ontology). These resources will be joined together into workflows to automate analysis in a flexible and exchangeable form.
The development of dynamic ODE models will facilitate the in silico modelling of key signalling pathways (such as DNA damage) to identify areas of dysregulation within different pathways, assess the effect of mutations on signalling outputs and identify possible drug targets.
Each partner will produce different insights into systems contributing to cardiovascular disease by integrating and contrasting their results. The systems biology models will be exchanged with repositories such as BioModels at the European Bioinformatics Institute (EBI) in order to be evaluated by scientists from around the world.